In the race for AI dominance, Meta pivoted from its world-class, energy-efficient data center designs to rapidly deployable "tents." This strategic shift demonstrates that speed of deployment for new GPU clusters is now more critical to winning than long-term operational cost efficiency.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

While AI inference can be decentralized, training the most powerful models demands extreme centralization of compute. The necessity for high-bandwidth, low-latency communication between GPUs means the best models are trained by concentrating hardware in the smallest possible physical space, a direct contradiction to decentralized ideals.

The current focus on building massive, centralized AI training clusters represents the 'mainframe' era of AI. The next three years will see a shift toward a distributed model, similar to computing's move from mainframes to PCs. This involves pushing smaller, efficient inference models out to a wide array of devices.

The intense power demands of AI inference will push data centers to adopt the "heterogeneous compute" model from mobile phones. Instead of a single GPU architecture, data centers will use disaggregated, specialized chips for different tasks to maximize power efficiency, creating a post-GPU era.

The limiting factor for large-scale AI compute is no longer physical space but the availability of electrical power. As a result, the industry now sizes and discusses data center capacity and deals in terms of megawatts, reflecting the primary constraint on growth.

Contrary to the idea that infrastructure problems get commoditized, AI inference is growing more complex. This is driven by three factors: (1) increasing model scale (multi-trillion parameters), (2) greater diversity in model architectures and hardware, and (3) the shift to agentic systems that require managing long-lived, unpredictable state.

The infrastructure demands of AI have caused an exponential increase in data center scale. Two years ago, a 1-megawatt facility was considered a good size. Today, a large AI data center is a 1-gigawatt facility—a 1000-fold increase. This rapid escalation underscores the immense and expensive capital investment required to power AI.

As single data centers hit power limits, AI training clusters are expanding across locations hundreds of kilometers apart. This "scale across" model creates a new engineering challenge: preventing packet loss, which can ruin expensive training runs. The solution lies in silicon-level innovations like deep buffering to maintain coherence over long distances.